Handbook of Graphical Models (Chapman & Hall/CRC Handbooks of Modern Statistical Methods) - Softcover
Book 18 of 27: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Synopsis
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference.
While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art.
Features:
- Contributions by leading researchers from a range of disciplines
- Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications
- Balanced coverage of concepts, theory, methods, examples, and applications
- Chapters can be read mostly independently, while cross-references highlight connections
The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.
"synopsis" may belong to another edition of this title.
About the Author
Marloes Maathuis is Professor of Statistics at ETH Zurich.
Mathias Drton is Professor of Statistics at the University of Copenhagen and the University of Washington.
Steffen Lauritzen is Professor of Statistics at the University of Copenhagen.
Martin Wainwright is Chancellor's Professor at the University of Berkeley.
"About this title" may belong to another edition of this title.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference.While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art.Features:Contributions by leading researchers from a range of disciplinesStructured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applicationsBalanced coverage of concepts, theory, methods, examples, and applicationsChapters can be read mostly independently, while cross-references highlight connectionsThe handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models. 556 pp. Englisch. Seller Inventory # 9780367732608
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